The ability to measure physical activity through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. The aim of this work is to assess the accuracy of seven commercially available wrist-worn devices in estimating heart rate (HR) and energy expenditure (EE) and to propose a wearable sensor evaluation framework. We evaluated the Apple Watch, Basis Peak, Fitbit Surge, Microsoft Band, Mio Alpha 2, PulseOn, and Samsung Gear S2. Participants wore devices while being simultaneously assessed with continuous telemetry and indirect calorimetry while sitting, walking, running, and cycling. Sixty volunteers (29 male, 31 female, age 38 ± 11 years) of diverse age, height, weight, skin tone, and fitness level were selected. Error in HR and EE was computed for each subject/device/activity combination. Devices reported the lowest error for cycling and the highest for walking. Device error was higher for males, greater body mass index, darker skin tone, and walking. Six of the devices achieved a median error for HR below 5% during cycling. No device achieved an error in EE below 20 percent. The Apple Watch achieved the lowest overall error in both HR and EE, while the Samsung Gear S2 reported the highest. In conclusion, most wrist-worn devices adequately measure HR in laboratory-based activities, but poorly estimate EE, suggesting caution in the use of EE measurements as part of health improvement programs. We propose reference standards for the validation of consumer health devices (http://precision.stanford.edu/).

TF-MoDISco (Transcription Factor Motif Discovery from Importance Scores) is an algorithm for identifying motifs from basepair-level importance scores computed on genomic sequence data. This technical note focuses on version v0.5.6.5. The implementation is available at https://github.com/kundajelab/tfmodisco/tree/v0.5.6.5

Somatic cell transcription factors are critical to maintaining cellular identity and constitute a barrier to human somatic cell reprogramming; yet a comprehensive understanding of the mechanism of action is lacking. To gain insight, we examined epigenome remodeling at the onset of human nuclear reprogramming by profiling human fibroblasts after fusion with murine embryonic stem cells (ESCs). By assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing we identified enrichment for the activator protein 1 (AP-1) transcription factor c-Jun at regions of early transient accessibility at fibroblast-specific enhancers. Expression of a dominant negative AP-1 mutant (dnAP-1) reduced accessibility and expression of fibroblast genes, overcoming the barrier to reprogramming. Remarkably, efficient reprogramming of human fibroblasts to induced pluripotent stem cells was achieved by transduction with vectors expressing SOX2, KLF4, and inducible dnAP-1, demonstrating that dnAP-1 can substitute for exogenous human OCT4. Mechanistically, we show that the AP-1 component c-Jun has two unexpected temporally distinct functions in human reprogramming: 1) to potentiate fibroblast enhancer accessibility and fibroblast-specific gene expression, and 2) to bind to and repress OCT4 as a complex with MBD3. Our findings highlight AP-1 as a previously unrecognized potent dual gatekeeper of the somatic cell state. Significance Reprogramming of somatic cells holds tremendous therapeutic promise. Here we show that a key barrier to this process is a DNA binding protein named activator protein 1 (AP-1). We find that AP-1 acts sequentially to first maintain the somatic state by turning genes on and then to block the initiation of the new cellular program by acting as a repressor of gene transcription. Crucially, we were able to uncover this feature of AP-1 by using the heterokaryon system of reprogramming, in which human fibroblasts are fused to an excess of mouse embryonic stem cells to favor the induction of the embryonic stem cell program in the fibroblast.

Background: Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable; however, current risk stratification tools (CHA ₂ DS ₂ -VASc) do not include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS). Methods: Using data from the largest available genome-wide association study in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank, both independently and integrated with clinical risk factors. The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Results: Compared with the currently recommended risk tool (CHA ₂ DS ₂ -VASc), the integrated tool significantly improved Net Reclassification Index (2.3% [95% CI, 1.3%–3.0%]) and fit (χ ² P =0.002). Using this improved tool, >115 000 people with AF would have improved risk classification in the United States. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (hazard ratio, 1.13 per 1 SD [95% CI, 1.06–1.23]). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson correlation coefficient, −0.018). Conclusions: In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors; however, the prediction of stroke remains challenging.

Copy number variation (CNV) at the 16p11.2 locus is associated with neuropsychiatric disorders, such as autism spectrum disorder and schizophrenia. CNVs of the 16p gene can manifest in opposing head sizes. Carriers of 16p11.2 deletion tend to have macrocephaly (or brain enlargement), while those with 16p11.2 duplication frequently have microcephaly. Increases in both gray and white matter volume have been observed in brain imaging studies in 16p11.2 deletion carriers with macrocephaly. Here, we use human induced pluripotent stem cells (hiPSCs) derived from controls and subjects with 16p11.2 deletion and 16p11.2 duplication to understand the underlying mechanisms regulating brain overgrowth. To model both gray and white matter, we differentiated patient-derived iPSCs into neural progenitor cells (NPCs) and oligodendrocyte progenitor cells (OPCs). In both NPCs and OPCs, we show that CD47 (a “don’t eat me” signal) is overexpressed in the 16p11.2 deletion carriers contributing to reduced phagocytosis both in vitro and in vivo. Furthermore, 16p11.2 deletion NPCs and OPCs up-regulate cell surface expression of calreticulin (a prophagocytic “eat me” signal) and its binding sites, indicating that these cells should be phagocytosed but fail to be eliminated due to elevations in CD47. Treatment of 16p11.2 deletion NPCs and OPCs with an anti-CD47 antibody to block CD47 restores phagocytosis to control levels. While the CD47 pathway is commonly implicated in cancer progression, we document a role for CD47 in psychiatric disorders associated with brain overgrowth.